Create a consensus tree from several hierarchical random graph models
consensus_tree creates a consensus tree from several fitted
hierarchical random graph models, using phylogeny methods. If the
argument is given and
start is set to
TRUE, then it starts
sampling from the given HRG. Otherwise it optimizes the HRG log-likelihood
first, and then samples starting from the optimum.
consensus_tree(graph, hrg = NULL, start = FALSE, num.samples = 10000)
- The graph the models were fitted to.
- A hierarchical random graph model, in the form of an
consensus_treeallows this to be
NULLas well, then a HRG is fitted to the graph first, from a random starting point.
- Logical, whether to start the fitting/sampling from the
igraphHRGobject, or from a random starting point.
- Number of samples to use for consensus generation or missing edge prediction.
consensus_treereturns a list of two objects. The first is an
igraphHRGConsensusobject, the second is an
igraphHRGConsensusobject has the following members:
parents For each vertex, the id of its parent vertex is stored, or zero, if the vertex is the root vertex in the tree. The first n vertex ids (from 0) refer to the original vertices of the graph, the other ids refer to vertex groups. weights Numeric vector, counts the number of times a given tree split occured in the generated network samples, for each internal vertices. The order is the same as in the